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In this special solo episode recorded at Q2B Paris 2024, Sebastian talks with Houlong Zhuang, assistant professor at Arizona State University, about his work in material science.

- Dr. Zhuang discusses his research on using quantum computing and machine learning to simulate high entropy alloy materials. The goal is to efficiently predict material properties and discover new material compositions.
- Density functional theory (DFT) is a commonly used classical computational method for materials simulations. However, it struggles with strongly correlated electronic states. Quantum computers have the potential to efficiently simulate these challenging quantum interactions.
- The research uses classical machine learning models trained on experimental data to narrow down the vast combinatorial space of possible high entropy alloy compositions to a smaller set of promising candidates. This is an important screening step.
- Quantum machine learning and quantum simulation are then proposed to further refine the predictions and simulate the quantum interactions in the materials more accurately than classical DFT. This may enable prediction of properties like stability and elastic constants.
- Key challenges include the high dimensionality of the material composition space and the noise/errors in current quantum hardware. Hybrid quantum-classical algorithms leveraging the strengths of both are a promising near-term approach.
- Ultimately, the vision is to enable inverse design - using the models to discover tailored material compositions with desired properties, potentially reducing experimental trial-and-error. This requires highly accurate, explainable models.
- In the near-term, quantum advantage may be realized for specific local properties or excited states leveraging locality of interactions. Fully fault-tolerant quantum computers are likely needed for complete replacement of classical DFT.
- Continued development of techniques like compact mappings, efficient quantum circuit compilations, active learning, and quantum embeddings of local strongly correlated regions will be key to advancing practical quantum simulation of realistic materials.

In summary, strategically combining machine learning, quantum computing, and domain knowledge of materials is a promising path to accelerating materials discovery, but significant research challenges remain to be overcome through improved algorithms and hardware. A hybrid paradigm will likely be optimal in the coming years.

Some of Dr. Zhuang's papers include:

Some of Dr. Zhuang's papers include:

Sudoku-inspired high-Shannon-entropy alloys

Machine-learning phase prediction of high-entropy alloys

Composer

Omar Costa Hamido

OCH is a performer, composer, and technologist, working primarily in multimedia and improvisation. His current research is on quantum computing and music composition, telematics, and multimedia. He is passionate about emerging technology, cinema, teaching, and performing new works. He earned his PhD in Integrated Composition, Improvisation and Technology at University of California, Irvine with his research project Adventures in Quantumland (quantumland.art). He also earned his MA in Music Theory and Composition at ESMAE-IPP Portugal with his research on the relations between music and painting. In recent years, his work has been recognized with grants and awards from MSCA, Fulbright, Fundação para a Ciência e a Tecnologia, Medici, Beall Center for Art+Technology, and IBM.

Your hosts, Sebastian Hassinger and Kevin Rowney, interview brilliant research scientists, software developers, engineers and others actively exploring the possibilities of our new quantum era. We will cover topics in quantum computing, networking and sensing, focusing on hardware, algorithms and general theory. The show aims for accessibility - neither of us are physicists! - and we'll try to provide context for the terminology and glimpses at the fascinating history of this new field as it evolves in real time.

The New Quantum Era, a podcast by Sebastian Hassinger. And Kevin Rowney.

Sebastian Hassinger:Hello, and welcome, listeners, to a special episode of the podcast. It's just me, Sebastian. Kevin is unavailable this week, and we've been trying to keep to a more regular biweekly schedule, so we agreed that I should just go ahead. I was in Paris last week for q2b 2024. It was the 2nd year of the conference.

Sebastian Hassinger:It's an international expansion of q2b Silicon Valley, which QC Ware began in 2017 as an attempt to explain quantum technologies and their progress to enterprise customers. At the 2017 conference, John Preskill actually gave the keynote, and that's where he coined the term NISC or noisy intermediate scale scale quantum computers. Q2b Silicon Valley in 2023, this past November, was, actually when the Michelukin, Vlutich logical cubits paper was published. Of course, we talked to Vlutich, a couple episodes ago. That was a great episode.

Sebastian Hassinger:This conference really continued to demonstrate, increasing levels of sophistication, I would say, in the techniques and the technology itself. There are quite a few really interesting talks that combined sort of state of the art classical approaches to simulation with forward looking quantum approaches that are anticipating, you know, these logical cubits and more capable devices that will be able to be combined eventually in deployment production deployments. So it's a real sense of sort of preparing for the post NISC era that we're we're starting on now. One researcher who presented some truly interesting work along these lines in material science was Holong Zhuang from Arizona State University. So at one point after a break, I grabbed him, and, took him into a private room that was very kindly provided by QC Ware.

Sebastian Hassinger:Thank you, Sandrine. I owe you one. So what follows is our conversation. Please forgive the room tone. I was just recording on my laptop, but I think it's fairly listenable, so please enjoy.

Sebastian Hassinger:Okay. We're here at q2b Paris 2024, 2nd year of the conference. It's put on by QC Ware. It's a great event. We've been having a bunch of fantastic talks, and it's really great to see everybody here and and sort of hear what's going on in the industry.

Sebastian Hassinger:And I I caught a talk yesterday that was really interesting, by Hulong Zhao, who's here with me now. He's from Arizona State University. And, Vuong, thank you for joining me for the conversation. How did you get started in quantum computing? What's sort of your path to getting here?

Houlong Zhuang:So let me introduce myself first. Yeah. So my name is Hongmong Zhuang. I'm assistant professor from Arizona State University. So how did I get into quantum computing?

Houlong Zhuang:I started as, when I first joined ASU, I started teaching a very basic mathematic class. I told you yesterday, it's linear algebra. And my initial purpose, teaching that class was I was very interested in machine learning. Right. And machine learning basically is applied

Sebastian Hassinger:Right.

Houlong Zhuang:Linear algebra. So yeah. I yeah. I had a chance to teach that class. Right.

Houlong Zhuang:And then after I taught that class, I realized, oh, linear algebra can be applied to so many different

Sebastian Hassinger:It's kind of like the native language of cubits. Right? I mean, that's kind of the you know, instead of Boolean algebra, you're actually using linear algebra when you're when you're doing a quantum algorithm.

Houlong Zhuang:Exactly. So for example, all the gates I mean, people call gates in quantum computing. Right. But, essentially, they are unitary unitary matrices in linear algebra.

Sebastian Hassinger:Right.

Houlong Zhuang:So if you know linear algebra very well, then it's automatically you can

Sebastian Hassinger:It's linear algebra machine.

Houlong Zhuang:Yeah. And then I was trained in density functional simulation, computational material design. Right. Right. Basically, you apply linear algebra again Right.

Houlong Zhuang:To solve this eigenvalue problem.

Sebastian Hassinger:Right.

Houlong Zhuang:When you solve this eigenvalue problem, you know that I, you know the eigenvalues. And eigenvalues can be interpreted as the energy network of electrons.

Sebastian Hassinger:Right.

Houlong Zhuang:Then even you don't do the experiment, then you can predict the properties of materials.

Sebastian Hassinger:Right. Right. Yeah. When when did the DFT sort of approach to material science? When was that, you know, invented?

Sebastian Hassinger:So or explore or discovered, I guess, science.

Houlong Zhuang:It was it it it dates back to 1960. The original paper, if I remember correctly, they were published in 19 sixties. Mhmm. And 2, one of the inventors so he received Nobel Prize in chemistry in 19 eighties. Right.

Houlong Zhuang:Right. Yeah. And this is a very, very efficient way to solve this context running or And

Sebastian Hassinger:but it is like, it's can be very difficult computationally, though. Right? Like, on a classical computer, a DFT problem can very easily outscale the the size of the computer you have, which is why quantum computing is sort of interesting for material science.

Houlong Zhuang:Yeah. It depends on, like, the level of theory. Mhmm. Right? Because DFT standard DFT so one of the most challenging power we call is training correlation function.

Houlong Zhuang:Essentially, it's how electron interact with each other. Mhmm. Right? They interact each other in 2 ways. One is the classical way.

Houlong Zhuang:Mhmm. So classic way by classical way, I I mean, you are negative charge. I'm also negative charge. So we apply Yeah. Now.

Houlong Zhuang:Yeah. That's not classical. Yeah. But the quantum part of interaction is very complicated. Yeah.

Houlong Zhuang:We don't know exactly what's going on. Right. So that's why we use another approximation even in, DMT in practical DMT. In principle, DMT is accurate theory. Which means that if you know the interaction, you can solve it accurately.

Houlong Zhuang:Right. But we don't know the interaction, so we need to apply some approximation to to approximate this interaction. And we know that this approximation can be applied to certain system or some complicated system. We all we call it strong correlation system. Right.

Houlong Zhuang:And this kind of approximation Right. It does not work.

Sebastian Hassinger:Right.

Houlong Zhuang:So then you keep on adding more and more approximation. So it starts to deviate from original.

Sebastian Hassinger:Yeah. It's similar to the problem in chemistry. Like, computational chemistry, the you're building approximation on top of approximation. Eventually, your your accuracy your results are just not gonna have to be useful. So it's similar with material science.

Houlong Zhuang:Yeah. Yeah.

Sebastian Hassinger:Yeah. So And if you have an accurate DFT calculation, what does that tell you about the what does that predict about the materials characteristics?

Houlong Zhuang:Oh, that's very good question. So, essentially, all you need for input to, compute classical computer simulation is the atomic arrangements. Mhmm. Right? So you can, for example, you can input the, atomic arrangement on carbon actin Mhmm.

Houlong Zhuang:In the way of diamond. Mhmm. Mhmm. Right? And you can also input the arrangement of carbon actin in a way, graphite.

Sebastian Hassinger:Mhmm. Right.

Houlong Zhuang:Right? Right. And then you can yeah, you can input those 2 structures to DMT program, and DMT program will give you 2 energies.

Sebastian Hassinger:I see.

Houlong Zhuang:And then it will tell you which energy is, no energy.

Sebastian Hassinger:Right. Right.

Houlong Zhuang:So in principle, no energy means more stable structure.

Sebastian Hassinger:Right. Okay.

Houlong Zhuang:Yeah. So which means that you can input the arbitrary combination of atoms Yeah. Or species.

Sebastian Hassinger:Yeah. Yeah.

Houlong Zhuang:Yeah. It will give you some energy. Yeah.

Sebastian Hassinger:And from that energy, can you can derive sort of the characters. Like, diamond is a very hard substance and and graphite is softer. Like, that that can be derived from the the the the the from those energy values?

Houlong Zhuang:Yeah. Actually, so there are several mainstream DLD programs for a solid state camp community. Mhmm. For example, Conde Espresso, VAS. Right.

Houlong Zhuang:Yeah. So or from Cambridge.

Sebastian Hassinger:Okay.

Houlong Zhuang:Actually, all those programs, they initially they are called the total energy based.

Sebastian Hassinger:Mhmm. Okay.

Houlong Zhuang:We would you go back to your question? Okay. So everything on many, many properties can be derived from

Sebastian Hassinger:total energy. That's cool.

Houlong Zhuang:So, for example, elastic constants can be written in terms of some derivatives of total energy.

Sebastian Hassinger:That's cool. That's really cool. So okay. So Yeah. So that's a a foundation on DFT material science.

Sebastian Hassinger:So you're now, looking at a way of using quantum computers to, to solve certain aspects of or challenges around DFT. Right?

Houlong Zhuang:Yes.

Sebastian Hassinger:And how how are

Houlong Zhuang:you doing that?

Sebastian Hassinger:So We're hoping to do that.

Houlong Zhuang:Yeah. Hoping to do that. Yeah. I I mentioned earlier, the most difficult part is the quantum interaction one molecule interact chemically interact with some substrate material Yeah. You know, some charge transfer.

Sebastian Hassinger:Right.

Houlong Zhuang:And you cannot use this standard version of this functional to describe these interactions. Okay. So in that sense, you need to use this very complicated many body theory.

Sebastian Hassinger:What's an example of that that type of molecule interacting with the substrate? What is that like a like a composite material or the with the layers? Is that that sort of thing? Oh, oh, many examples.

Houlong Zhuang:So for example, use carbon dioxide molecule is

Sebastian Hassinger:Okay.

Houlong Zhuang:Chemically of the salt. So I see. Ionic liquid. Yeah. When they as far as an age start to have some charge transfer, your electron become mine.

Houlong Zhuang:My initial become yours.

Sebastian Hassinger:I see.

Houlong Zhuang:Right? So in that's wrong in that's wrong, in interaction start to have to be very complicated.

Sebastian Hassinger:Right. Okay.

Houlong Zhuang:Yes.

Sebastian Hassinger:Interesting. So okay. So it's broadly applicable. It's very you have to do the quantum calculation, which is quite complicated. So you're looking for a computationally efficient way to do that on on quantum computers.

Houlong Zhuang:And then you can of course, you can do, very complicated quantum chemistry by adding more and more many body. We call it many body facts. Right. And and quantum computers, it they are promising to help solve this, to implement this, for example, CCSP is couple cluster method. Okay.

Sebastian Hassinger:Yeah. Interesting. Interesting. So okay. So what's what is unique about your approach then?

Sebastian Hassinger:What's what's your research focused on?

Houlong Zhuang:Oh, so my talk yesterday was about a system called a high entropy material. Mhmm. So it's different from conventional alloy. So conventional alloy, like in a steel Mhmm. Yeah.

Houlong Zhuang:Make, which maintenance chair you are sitting. Right.

Sebastian Hassinger:Yeah. Yeah. I'm familiar with steel.

Houlong Zhuang:Yeah. Yeah. It's dominant atomized, iron, maybe 90%. I see. And there's some tiny amount

Sebastian Hassinger:of current.

Houlong Zhuang:I see. Okay. Zero less than 0.3%. Okay. We call it conventional alloy.

Houlong Zhuang:Okay. One dominant. Right.

Sebastian Hassinger:Okay. And then

Houlong Zhuang:very small amount of elements. Got it. And in contrast to this conventional element, we have this high end of the alloy, so which means that you have many, many elements. So the original experiment doing this, high entropy material system is one undergraduate student from UK University. So he was doing some undergraduate thesis.

Houlong Zhuang:So his adviser, asked him to mix more than 20 elements together. Just mix together to see what you can get out of that.

Sebastian Hassinger:It reminds me of, you know, sort of being a kid and, like, mixing stuff in the kitchen to make it like a magic potion. Just throwing a bunch of spices in and seeing what happens.

Houlong Zhuang:Exactly. Like, the mall is different. Right? Yeah. Just now you you go to some buffet.

Houlong Zhuang:You eat. So many varieties that you mix together.

Sebastian Hassinger:Make a new food.

Houlong Zhuang:Yeah. Or may maybe you can make your stomach upset.

Sebastian Hassinger:Okay. So what what did he get when he mixed together the 20 elements?

Houlong Zhuang:The original article actually not much. Yeah. Like, in the waiting Yeah. It just very yeah. So exactly.

Houlong Zhuang:Just, yeah, just a new way. But after maybe maybe on a in the same year, a Taiwanese group, may independently did similar experiment, a mix different element. Mhmm. Yeah. And then from then on, more and more people start with all these so called computational space.

Houlong Zhuang:Mhmm. But you can imagine each element is one axis in a high dimensional space.

Sebastian Hassinger:Right. Right. Which starts to sound like the Hilbert space.

Houlong Zhuang:Exactly. Exactly. Yeah. Yeah. It's yeah.

Houlong Zhuang:Here, but it's very exact.

Sebastian Hassinger:As soon as you start saying high dimensionality, I think of of, you know, cubits all in entanglement.

Houlong Zhuang:Yeah. So you think hydrogen is x axis. Right? Helium is y axis. Right.

Houlong Zhuang:Right? In the same group of elements. And they may they may not necessarily have orthogonal axis, but they may be closed. Right.

Sebastian Hassinger:Yeah. Very complicated.

Houlong Zhuang:Yeah. And then now this high end of the material, this family of material become very popular in material science community. You can find almost high and beyond everything. High and beyond, superconductor. High and beyond, to be semiconductor.

Houlong Zhuang:Interesting. And to be catalysis. Interesting.

Sebastian Hassinger:And what what why is it becoming so popular? Because there's so much that's unknown to be to be discovered about it. Is that

Houlong Zhuang:Yeah. You can imagine this each each anime is, like a is a pixel. Right?

Sebastian Hassinger:Okay. Yeah.

Houlong Zhuang:And then you can try to paint this a new material Cool. Using mixture of That's

Sebastian Hassinger:really cool.

Houlong Zhuang:Things always. But, I

Sebastian Hassinger:mean, it sounds like I mean, you said yesterday, in the talk that there's a combinatorial problem. Right? Yeah. You got n by n or m by n or whatever. There's, like, multi multi current kind of combinations just to start with.

Sebastian Hassinger:So is that you're starting with trying to narrow down the possibilities that are are for your sort of research space or your experimental space. Is that right?

Houlong Zhuang:Yes. So we originally start from classical machine learning. Mhmm. So we have some, training data set from experiment saying, oh, which combination of elements can give you some certain properties. Right.

Houlong Zhuang:And then we can train the deep learning model. Right. And then so that next time you have a new mixture of elements Right. I can I can predict Yeah? Yeah.

Houlong Zhuang:For you in a new property. Yeah. Yeah. And we started kind of very early. So we we I had a very good masters in the marketing business.

Houlong Zhuang:Mhmm. Average one of the really earliest article using this deep learning for discovering entropy and noise. Mhmm. And he was published in 2019. Now he has to be inside over 300 times.

Houlong Zhuang:Wow.

Sebastian Hassinger:Yeah. That's pretty good for 4 years. Exactly. That's great. Yeah.

Sebastian Hassinger:That's great. So that was the classical machine learning. Mhmm. Are you it was the next step to start adding in quantum simulation, or were you applying quantum to potentially get a better machine learning model?

Houlong Zhuang:Yeah. We had, we have already started the quantum machine learning model trying to achieve the same accuracy using available quantum simulator Right. Right. And quantum hardware. Actually, yesterday, I present some results.

Houlong Zhuang:Right. But we used a very adding very rudimentary encoding of the Mhmm. Entropy analytica. I So we simply we only use the chemical formulas.

Sebastian Hassinger:I see.

Houlong Zhuang:Yeah. But I think we can do a better job. For example, give some more input or more some more chemical intuition. Mhmm. Yeah.

Houlong Zhuang:So in our world, we haven't considered, oh, the relation if on a if some animal in the same group, right, they shouldn't be orthogonal in each other. Right? But the in the you know, what we are now, we we assume is Okay. Okay. Orthogonal in each other.

Sebastian Hassinger:So better better sort of, whatever, starting point, starting assumptions

Houlong Zhuang:about Yeah. Yeah. Or different ways of encoding this. Interesting. Yeah.

Sebastian Hassinger:And Is that challenging, by the way, with quantum machine learning. I mean, the the the preparation the state preparation is often really a lot of the overhead. Right? Because it's you have to fit that high dimensionality into a very small number of cubits. Is that is that sort of part of the challenge?

Houlong Zhuang:Yeah. It's part of the challenge. But I think another challenge comes from the noise of the

Sebastian Hassinger:Mhmm. Of course.

Houlong Zhuang:Yeah. And each material have, must can must be able to be retained in terms of its competition or chemical formula. Right? Right. We only we we have about 100 elements.

Houlong Zhuang:Mhmm. Which means that if we do 2 to the power of 7, you can you can cover all the elements in the

Sebastian Hassinger:Oh, to the power of 7 cubits? Or

Houlong Zhuang:Yeah. 2 7 cubits.

Sebastian Hassinger:Oh, 7. Wow.

Houlong Zhuang:Yeah. 7 cubits. Because you can use, one string of 0 Right. Numbers and 1

Sebastian Hassinger:It's all vectors.

Houlong Zhuang:Right? Yeah. Yeah. Yeah. I mean, the algebra.

Houlong Zhuang:Right. Right. Yeah.

Sebastian Hassinger:That's so I mean, I find that so fascinating. I think coming from a classical computing background, that's the hardest thing to sort of wrap your head around is is thinking in vectors, thinking in encoded vectors. And, of course, as you said, that's happening in classical in the area of machine learning, with with linear algebra. But it's still, like algorithmically, that feels like the the biggest barrier to entry for for people with a classical background is to start thinking in those that matrix, multiplication kind of space.

Houlong Zhuang:Yes.

Sebastian Hassinger:Right? I mean, like, Peter Shor's big, you know, sort of breakthrough is is having that incredible, you know, quantum phase estimation kind of black box where the the the, you know, the factorization happens almost magically. It's Exactly. Because of the way you approach it. It's such it's such a cool part of the whole theory.

Houlong Zhuang:Yeah. I taught a class and I assigned Peter Saw's algorithm as one of the humble problems. And one student in my class, so he did the exercise. So he basically, he decomposed 15 into 3 and 5. And he solved the problem, and he wrote me an email saying that he found a very powerful algorithm solving that problem.

Houlong Zhuang:That's the old that's the first this kind of comment I received from students. That's cool.

Sebastian Hassinger:Yeah. That's also by the way, that's, IBM Research in 2001, I think. That was the first successfully used of Shor's algorithm on 7 qubits on an NMR device, and they factored 15 to 35.

Houlong Zhuang:3 and 5.

Sebastian Hassinger:Yeah. Which doesn't sound like that impressive a task, but until you realize how they're doing it. Yeah. The algorithm

Houlong Zhuang:is pretty Yeah. The algorithm is really neat and meaningful.

Sebastian Hassinger:It really is.

Houlong Zhuang:Private. Wow. This guy deserves a genius.

Sebastian Hassinger:I know. And then and then coming up with the 1st error correction scheme, like, a year later, that's it was, it was quite a quite a productive year for Peter.

Houlong Zhuang:Yeah. He had a he had a track record in this kind of research. Right? Because before he he discovered or invented this algorithm, he was already very famous expert in

Sebastian Hassinger:Oh, really? Theory.

Houlong Zhuang:Okay. So he published a number of papers or numbers.

Sebastian Hassinger:Interesting. That's funny because my focus is on quantum computing. I don't know anything about Peter Schwer before.

Houlong Zhuang:He was already the same as he got.

Sebastian Hassinger:That's interesting. Yeah. It makes sense.

Houlong Zhuang:You probably that

Sebastian Hassinger:seem brilliant. You don't just be that brilliant overnight. Right? Yeah.

Houlong Zhuang:So that's probably why, like, he naturally. Right. Yeah. I cannot believe that.

Sebastian Hassinger:But anyway, returning your research. Let's get back to the material science. Okay. So you had this classical machine learning, sort of down selector attempt to predict the the the properties. You're now looking at quantum machine learning, replication or or, you know, enhancement of that potentially.

Houlong Zhuang:Yeah.

Sebastian Hassinger:So then you were talking about from that smaller pool of potential materials is the next step to try to simulate using a quantum computer, the the DFT

Houlong Zhuang:type of We we are going to first simulate using DFT, but it's also where we can use quantum computer. Okay. Because when you do DLT simulation, you need to have a input,

Sebastian Hassinger:Okay.

Houlong Zhuang:Yeah. Gas, arrange Right. Right. Atoms. Right.

Houlong Zhuang:Right? And then you have many elements in we call it supercell. Mhmm. Essentially, you can imagine this is a cubic grid. Right?

Houlong Zhuang:Mhmm. And in each grid point, you decorate with some element. Mhmm. And you there are so many different combinations. You said.

Houlong Zhuang:Yeah. Yeah. Yeah. Yeah. Yeah.

Houlong Zhuang:You have different permutation. You're still representing the same compositions. Mhmm. Right? We are not going to do all the permutation.

Houlong Zhuang:Right? It's going to be infinite permutations. Right. Yeah. And that's probably where you can use, condens simulator

Sebastian Hassinger:I see.

Houlong Zhuang:To come up with some statistical

Sebastian Hassinger:Okay. So the machine learning step, whether it's classical or quantum, narrows down the candidates. The quantum steps similar or or sorry, runs predictions of of what that initial decorated state would be, the super cell state?

Houlong Zhuang:Yeah. Exactly.

Sebastian Hassinger:And then and then that's the input to the DFT.

Houlong Zhuang:Exactly. I see. Exactly.

Sebastian Hassinger:Is there a quantum algorithm that's equivalent to DFT? Is there is there a q DFT?

Houlong Zhuang:Very good question. I saw some papers. Mhmm. So for example, they apply quantum quantum counterpart of DFT. Quantum, not necessarily DFT.

Houlong Zhuang:Quantum counterpart, we call it DFT based molecular simulations. Okay. Yeah. Interesting. That's this kind of very general natural extension.

Houlong Zhuang:Mhmm. Mhmm. Mhmm. Right? We know, quantum gametes, they probably good for something.

Houlong Zhuang:I can value.

Sebastian Hassinger:Right. Right.

Houlong Zhuang:Right. Right. Yeah.

Sebastian Hassinger:And then If you wanna simulate a quantum system, you a quantum system.

Houlong Zhuang:You need to get the eigenvalue. So Yeah. Yeah.

Sebastian Hassinger:Interesting. And so, like, what is this possible on on sort of NISC machines, or is this something that would need fault tolerant cubits to an error correction to to scale?

Houlong Zhuang:So in principle, you need the

Sebastian Hassinger:You need fault tolerant?

Houlong Zhuang:Yeah. You need the yeah. Like, in the yeah. Like, one slide. Sort of, some, this morning.

Sebastian Hassinger:Yeah. Right. Yeah. That was, it was QW at Pharmaceutical.

Houlong Zhuang:Yeah. Yeah. Yeah. Yeah. So basically, he's doing similar things.

Houlong Zhuang:Right. But he's doing he's working on this, molecular system.

Sebastian Hassinger:Right.

Houlong Zhuang:So mind disease is mostly solid system, which means you can repeat.

Sebastian Hassinger:Okay. Interesting. Because the pattern repeat the structure repeats over and over again.

Houlong Zhuang:Yeah. I understand. I see. Some of copper. Right?

Houlong Zhuang:It's Right. Yeah. It's it's periodic.

Sebastian Hassinger:Right. Right. Interesting. Interesting.

Houlong Zhuang:Yeah.

Sebastian Hassinger:Cool. So okay. So, then what what's sort of the scale of of logical qubits that you would need to do something that's that has clear advantage over any kind of classical approach, do you think?

Houlong Zhuang:So so far, I think if I have 7 perfect cubits Oh, really? That's small number. Yeah. So I can I can include everything? Okay.

Sebastian Hassinger:You were saying? Anybody real Yeah.

Houlong Zhuang:Right? As far as they have something Interesting. Quantum circuit. That's really interesting. I think in the paper we published, we saw some preliminary law.

Houlong Zhuang:So we input the chemical formula, and if it is implemented in quantum simulator, only the bars representing the element showing up. Only other bars. Yeah. And they don't show up. Each bar here means, one string.

Houlong Zhuang:Okay. 0s and ones. Okay. It's each element. Right.

Houlong Zhuang:Okay. So, basically, let's say you have a b c three elements. Yeah. And then they they model ratio is 1 to 1 to 1. Mhmm.

Houlong Zhuang:And then you can do this encoding to a quantum circuit, and then you measure it right away. And in the end, you will only see 3 bars. Mhmm. Many, many times. Those 3 bars, we are having the same amplitudes.

Houlong Zhuang:Yeah. And we do it. Yeah. Because of noise, all possible zeros and ones may start to slow up.

Sebastian Hassinger:I see.

Houlong Zhuang:Yeah.

Sebastian Hassinger:So but then so you can simulate 7 perfect cubits. There's no performance advantage on a on a classical simulator, or they're obviously doing just a straight classical way? Or is there is there some is there some, like, algorithm that or approach that you're using that that has advantage in either either class or a quantum way then?

Houlong Zhuang:Yeah. So so far, we are looking at the classical Yeah. Content content algorithm. I by classical content algorithm, like, I mean, yeah, like Peter saw Right. Rovers algorithm.

Houlong Zhuang:Yeah. So we are looking at oh, how can you let's say you have 3 bars showing up by after you do after you many slots. Right. How can you change those 3 bars into 1 bar, and then that one bar representing some kind of encoding? Interesting.

Houlong Zhuang:Category. Okay. Right? So no matter

Sebastian Hassinger:It's almost a compression method.

Houlong Zhuang:Yeah. And it also all these quadratic

Sebastian Hassinger:so it's probably Okay. Quadratic search. Yeah. Yeah. Yeah.

Houlong Zhuang:That that has been sung by, Grover. Right, Grover. Yeah. Yeah. Yeah.

Houlong Zhuang:Yeah. I think that's where we can possibly see quantum quantum.

Sebastian Hassinger:And and what would, like, the ideal sort of outcome in your mind? Is it is it some sort of approach to be able to, you know, efficiently, predict the the alloy composition that would have the high entry entry alloy composition, would have certain characteristics that you you desire, basically, sort of design your materials, if you will. Is that is that kinda what you're trying to do?

Houlong Zhuang:Yeah. Exactly. So let's say you train your you train your condensate circuit using, a vital word error or good catalysis or bad catalysis. Mhmm. Mhmm.

Houlong Zhuang:Right? And then in the end, you will come up with some new common teacher. Right? Kind of like extrapolation. Yeah.

Houlong Zhuang:I'm very interested in recent, classical machine learning algorithm. DVD Yeah. Yeah. Yeah. It's it's generated models.

Sebastian Hassinger:Right.

Houlong Zhuang:Yeah. You can come up with so you learn the probability distribution of classical data, and then you take one point from the probability distribution, and then you you based on that point, you can come up with some new thing.

Sebastian Hassinger:I see. And then you would sort of that would be the input into the into the quantum circuit, to to predict the or simulate the those character or the certainly, the the, the supercell.

Houlong Zhuang:Yeah. Yeah. And there are some recent research on people have started published some paper on so called quantum attention mechanism.

Sebastian Hassinger:Mhmm. Mhmm.

Houlong Zhuang:Yeah. So, essentially right? So in in classical machine learning, each token representing, for example, each English word Mhmm. Right? So may have some kind of correlation.

Houlong Zhuang:Mhmm. Mhmm. So for example, you say a sentence. This word

Sebastian Hassinger:Right.

Houlong Zhuang:Pays attention to more attention to another word.

Sebastian Hassinger:Right. Right. Right.

Houlong Zhuang:Right. So you can imagine each word is a particle. Right. Yeah. Or not necessarily a quantum particle.

Houlong Zhuang:Right. That's what I even think is a classical particle. Right. Right? And then you start to have this, correlation.

Sebastian Hassinger:That's interesting.

Houlong Zhuang:Yeah. Of this sense.

Sebastian Hassinger:I love I mean, what I really find fascinating is that that you're combining sort of cutting edge classical techniques with these emerging quantum techniques.

Houlong Zhuang:I mean,

Sebastian Hassinger:I feel like that that's going to be you know, when we get to some kind of working quantum advantage for some, you know, material science or chemistry or other scientific physical science application, it is gonna end up being some very tightly bound combination of classical and quantum that actually delivers that that resolve. Right? Exactly. Yeah. Very interesting.

Sebastian Hassinger:Well, that's fantastic. You also mentioned yesterday when we were talking, the the stuff you're doing with the the summer program in Africa. Tell me a little bit about that because that sounded really fascinating.

Houlong Zhuang:Oh, yeah. So, so the whole overall overall program is called the assessment.

Sebastian Hassinger:Mhmm.

Houlong Zhuang:Yeah. A stands for Africa. And so, basically, this program is trying to collaborate with physicists in Africa That's right. To help promote DFT or electronic structure. Right.

Houlong Zhuang:Yeah. So it was originally started by, Richard Martin. Mhmm. I think he he retired Mhmm. Yeah, from UIUC.

Houlong Zhuang:He's very he has a very classic, book, in that Sonic segment.

Sebastian Hassinger:Right. The name is familiar.

Houlong Zhuang:Yeah. He is. Actually, you also mentioned the innovative, ABS innovative fund. Mhmm. Yesterday.

Houlong Zhuang:Yeah. I think he got a one of the Oh, really? Innovative fund for this electronic tractor. Maybe in the first yeah. Oh, wow.

Houlong Zhuang:Yeah. In the first version of this innovative

Sebastian Hassinger:Oh, that's great.

Houlong Zhuang:Yeah. So Right there.

Sebastian Hassinger:So he started this program, sort of collaborating with African physicists. And and what you've done in now 3 years, I think.

Houlong Zhuang:So he has been doing this.

Sebastian Hassinger:Oh, but you you've just taken

Houlong Zhuang:you started to be Yeah. I was planning to start before COVID, but because of Right. Yeah. So I only started

Sebastian Hassinger:slow travel day.

Houlong Zhuang:I already started last year Okay. In Hikari, Rwanda. Right. Right. So And

Sebastian Hassinger:what is what is it you do when you get go over there? It's, like, a couple weeks, you said. Right?

Houlong Zhuang:We did the 2 weeks. Yeah. Yeah. So each, teacher taught 1 module. Mhmm.

Houlong Zhuang:So some people taught the electronic structure. Mhmm. I did a small module about machine learning, yeah, presenting my research. Cool. And this year is gonna be Nigeria?

Houlong Zhuang:Yeah. This year, yeah, we're going to be Nigeria, and next year this year, we call it a mini assessment. Okay. Yeah. Only maybe 30 people focusing on Okay.

Houlong Zhuang:From Nigeria. But next year, we are probably going to do a much bigger version in I think it's in Ghana.

Sebastian Hassinger:Oh, cool. Cool. And this year, you're you're bringing your topic is gonna be quantum computing or something related to quantum computing?

Houlong Zhuang:Yeah. This year, I'm planning to teach student there some basic Excellent.

Sebastian Hassinger:Yeah. That's great. That's really great. And what sort of you said mini is 30 people. What's the sort of normal size then?

Houlong Zhuang:Normal size is about double or triple. Right. Yeah. Double or triple. But much more countries still in charge.

Sebastian Hassinger:I see. So you're bringing people to one location, but they're front coming from multiple countries. Got it.

Houlong Zhuang:So that means the 10 Africa or more than 10 African countries. Great.

Sebastian Hassinger:That's amazing. Were involved. That's really great. Well, fantastic. Thank you so much for joining me.

Sebastian Hassinger:This has been a really good conversation.

Houlong Zhuang:Thank you so much, Sebastian. You too.

Kevin Rowney:Okay. That's it for this episode of The New Quantum Era, a podcast by Sebastian Hassinger and Kevin Roney. Our cool theme music was composed and played by Omar Khosstah Hamido. Production work is done by our wonderful team over at Podfi. If you are at all like us and enjoy this rich, deep, and interesting topic, please subscribe to our podcast on whichever platform you may stream from.

Kevin Rowney:And even consider, if you like what you've heard today, reviewing us on iTunes and or mentioning us on your preferred social media platforms. We're just trying to get the word out on this fascinating topic and would really appreciate your help spreading the word and building community. Thank you so much for your time.